The global push for sustainable energy has heightened the demand for green hydrogen, which is crucial for decarbonizing heavy industry. However, current electrolysis plant capacities are insufficient. This research addresses the challenge through optimizing large-scale electrolysis construction via standardization, modularization, process optimization, and automation. This paper introduces H₂Giga, a project for mass-producing electrolyzers, and HyPLANT100, investigating large-scale electrolysis plant structure and construction processes. Modularizing electrolyzers enhances production efficiency and scalability. The integration of AutomationML facilitates seamless information exchange. A digital twin concept enables simulations, optimizations, and error identification before assembly. While construction site automation provides advantages, tasks like connection technologies and handling cables, tubes, and hoses require pre-assembly. This study identifies key tasks suitable for automation and estimating required components. The Enapter Multicore electrolyzer serves as a case study, showcasing robotic technology for tube fittings. In conclusion, this research underscores the significance of standardization, modularization, and automation in boosting the electrolysis production capacity for green hydrogen, contributing to ongoing efforts in decarbonizing the industrial sector and advancing the global energy transition.

Hydrogen is one of the main pillars in the transition to renewable energy and can be used in particular for buffering and storing energy. Electrolyzers are needed to produce sustainable, green hydrogen. Today, these electrolyzers are mainly manufactured by hand. An electrolyzer plant consists of two main components, the stack in which the actual electrolysis takes place and the balance of plant that ensures the operation of the stack. Different electrolysis technologies have essential similarities in the balance of plant so that automation can achieve optimization potentials at this point. Automation technologies such as industrial robots are intended to bring this production to series maturity. For this reason, large-scale electrolysis plants are analyzed with regard to their design, the combination of different electrolysis technologies, and the connection technology used. A significant proportion of the necessary assembly steps are cable, hose, and tube connections, which automation technology has not yet been able to assemble in a process-safe and economical manner. In this paper, the process of tube connection using tube nuts is explained in more detail, and a design system for a robotic tool is presented. This end-effector is designed for industrial robots and is intended to make series production more economical. For this purpose, the necessary information processing, material flows, and energy transformations are investigated, and their interrelationships are presented. This publication aims to evaluate a suitable physical operating principle for bolting hydrogen tube nuts.

Hydrogen is one of the main pillars in the transition to renewable energy and can be used in particular for buffering and storing energy. Electrolyzers are needed to produce sustainable, green hydrogen. Today, these electrolyzers are mainly manufactured by hand. An electrolyzer plant consists of two main com-ponents, the stack in which the actual electrolysis takes place and the balance of plant that ensures the operation of the stack. Different electrolysis technologies have essential similarities in the balance of plant so that automation can achieve particular optimization potentials at this point. The use of automation technologies such as industrial robots is intended to bring this production to series maturity. For this reason, large-scale electrolysis plants are analyzed with regard to their design, the combination of different elec-trolysis technologies and the connection technology used. A significant propor-tion of the necessary assembly steps are cable, hose and tube connections, which automation technology has not yet been able to assemble in a process-safe and economical manner. In this paper, the process of tube connection using tube nuts is explained in more detail and a design system for a robotic tool is presented. A robot tool is called an end-effector because it is used at the end of the robot kinematics. This end-effector is designed for use on industrial robots and is intended to make series production more economical. For this purpose, the necessary information pro-cessing, material flows and energy transformations are investigated and their in-terrelationships are presented. The goal is to evaluate a suitable physical operat-ing principle for bolting hydrogen tube nuts.

The increasing application of industrial robots in modern production systems contrasts with a persistently high programming complexity that requires specialized know-how and creates substantial entry barriers. This work addresses this problem by introducing a systematic approach to robot programming based on Large Language Models (LLMs) that automatically translates natural language task descriptions into executable robot programs. The solution follows a two-stage pipeline: in Stage 1, the LLM structures the input into coherent process steps, and in Stage 2 these process steps are transformed into C++ code using a high-level function library. The performance is evaluated in simulation for the automated electrical cabinet assembly use case with terminal blocks, which is a significant element of various production processes. The architecture, based on the Robot Operating System 2 (ROS2) and MoveIt2, further integrates a standardized AutomationML-based configuration management for dynamic parameter handling and persistent state storage. A graphical user interface visualizes intermediate results, enables manual interventions and enables a simple operation for potential users without programming experience. The evaluation of the presented approach shows a success rate of up to 95% for interpreting natural language instructions and generating code in the application scenario focused. The system reliably recognizes object attributes and correctly executes complex assembly instructions. In general, this work demonstrates how modern LLMs can bridge the semantic gap between human intent and robotic code for industrial applications. The developed high-level abstraction makes the system usable for non-programmers, highlights the potential for intuitive robot programming, and simultaneously identifies concrete technical challenges.

The achievable accuracy of industrial robots, both absolute and repeatability, is a critical quality parameter that determines the suitability of a system for specific applications. High precision is particularly essential in tasks such as robot-based measurement, milling, and grinding. One common method to enhance robotic accuracy is the use of external measurement systems, such as marker-based motion capture, which utilizes multiple camera systems and optical reference markers to determine spatial positions with a precision of up to micrometers via triangulation. However, the efficient integration of such systems remains a significant challenge. The placement of cameras and markers often relies on trial-and-error methods and expert intuition, often requiring physical mock-ups or real production environments. This results in a time-consuming, manual, and iterative process that delays system commissioning and hinders seamless integration into existing production workflows. This paper presents a holistic strategy for optimizing the planning of marker-based motion capture systems in industrial robotics. Beyond camera placement, the presented strategy considers the optimized positioning and alignment of individual markers, as well as the design of a volumetric body acting as a carrier for the reference markers. In detail, the addressed planning challenge is formulated as a mathematical optimization problem, followed by the solution strategy. While the solution strategy focuses on the joint camera and marker optimization, the experimental evaluation in this contribution considers a fixed marker set to assess the impact of camera optimization initially.